Rectal cancer surgery may result in anorectal and urogenital functional deficits which drastically diminish the quality of life of patients . These complications may result from damaging the pelvic autonomous nervous system .
Pelvic intraoperative neuromonitoring (pIONM) may help preserve the function of the pelvic autonomous nervous system during rectal cancer surgery. Two-dimensional pIONM could predict urinary and anorectal function after low anterior rectal resection  and intraoperative neuromonitoring was associated with preserving the urinary  and urogenital  function in patients undergoing open total mesorectal excision (TME).
pIONM relies on injecting electrical current in-situ into the autonomous nervous tissue which is at risk of damage during TME. In pIONM, electrical stimulation evokes a contraction of m. detrusor vesicae and a modulation of the internal anal sphincter (IAS) activity. Any aberration in these responses can warn the surgeon of possible nerve damage.
The surgeon needs to introduce the stimulation probe in situ in order to perform neuromonitoring. This procedure prolongs the surgery because pIONM can nowadays only be performed intermittently in the crucial phases of the surgical procedure. Unobserved nerve damage can occur in between the phases involving neuromapping . Thus, continuous methods of stimulation are desirable.
Transdermal stimulation of the sacral roots of the aforementioned autonomous nervous system might offer several advantages compared to in-situ stimulation. Electrodes could be permanently placed and continuously stimulate throughout the surgery. The surgeon could perform the standard surgery in parallel to the monitoring. This could facilitate near real-time reaction to possible nerve damage and increased practicability and usage of pIONM.
During surface stimulation, the contact area of the electrodes and the distance to the target autonomous neural structures are bigger than during in-situ stimulation, which casts doubt on the selectivity of stimulation. Knowing the distribution of current due to surface stimulation could help assess its usability in pIONM. Because direct measurement of the distribution of current in tissue is problematic, the chosen approach is the use of numerical modeling of the electric field by means of the finite element method (FEM) based on MRI data .
The aim of this contribution is to describe the preparation of a quality three-dimensional volumetric mesh to gain insight into the usability of surface stimulation in continuous pIONM.
2 Material and methods
In order to process the surface meshes of tissues obtained from MRI-data (the Duke model, the Virtual Population V2.0 ) and generate a volumetric mesh suitable for FEM, we used Octave open-source software (GNU) and the problem solving environment SCIRun with BioMesh3D (CIBC, University of Utah, USA).
We separately modelled the autonomous nerves controlling the function of the urinary bladder, the external and internal anal sphincters (IASs) because the Virtual Population 2.0 did not contain them. Based on ,  and clinical experience, we created a 3D model of the autonomous innervation of the IAS and the bladder (Figure 1). To obtain a model of each nerve, we created a set of Bézier curves and extruded a 3-mm circle normal to each curve (Figures 1 and 2).
We trimmed the resulting surfaces of all tissues to the region of interest of TME surgery . The model spanned from the sigmoid colon to the male reproductive organs.
We subsequently obtained the volume information inside the three-dimensional surface meshes using a ray-tracing algorithm and subdivided the entire volume into voxels (cuboid representations of the volume with 1 mm edge) according to the method described in  (Figure 3).
The resulting tetrahedral volumetric mesh consisted of autonomous nerve tissue, subcutaneous and visceral fat, skin, bone, cartilage, gastro-intestinal tract, muscle tissue, urinary bladder and cerebrospinal fluid (Table 1, Figure 5).
We supplemented the resulting volumetric mesh with electrode pads for the simulation of the sources of the electric field. We created the electrode model prototypes (three rectangular pads with rounded corners and 10 circular electrodes forming a stimulation array) using Blender®. Subsequently, we orthogonally projected the flat electrodes onto the surface of skin of the model in SCIRun® (Figure 6).
Next, we increased the density of the mesh near the contact between the tissue model and the modelled electrodes (Figure 7). We saved the resulting field in. fld format which can be directly read by the SCIRun software and is ready to perform the finite element method analysis.
We created a model of the pelvis minor whose dimensions correspond to the region in question during TME . The model consisted of tissue compartments whose electrical properties can be freely assigned. We supplemented the model with autonomous neural tissue based on ,  because the fine neural fibres were not included in the Virtual Population V2.0. We modelled the neural plexi as intersections of 3-mm tubular nerves. In reality, however, an intricate network of fine nerve fibres with small dimensions forms the autonomous nervous system . Such high complexity would be impractical, hard to interpret, and the resulting change of accuracy would be questionable.
The results of numerical modeling of the nerve activation due to ex-situ electrical stimulation of the cauda equina compared with animal studies . In the case of pIONM, numerical modeling could help assess and optimize the use of surface stimulation in non-invasive continuous monitoring. The proposed model will undergo FEM analysis of the electric field due to surface stimulation. It is crucial to assess the selectivity of ex-situ stimulation of the autonomous nervous system, its sensitivity and specificity to monitoring nerve damage.
A one-size-fits-all model may fail to predict universal guidelines for stimulation because of inter-individual differences of patients’ anatomy. The numerically predicted parameter ranges may need to be adjusted for individual patient. A system for automatic control of stimulation could help individually navigate the stimulation process.
We used SCIRun thanks to the National Institute of General Medical Sciences of the National Institutes of Health under grant number P41 GM103545-17.
Research funding: This contribution was funded under the autoPIN Project (BMBF, grant number 13GW0022C). Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent is not applicable. Ethical approval: The conducted research is not related to either human or animal use.
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About the article
Published Online: 2016-09-30
Published in Print: 2016-09-01
Citation Information: Current Directions in Biomedical Engineering, Volume 2, Issue 1, Pages 185–188, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2016-0042.
©2016 Tomasz Moszkowski et al., licensee De Gruyter.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0